Foreign Aid, Foreign Policy, and Domestic Government Legitimacy: Experimental Evidence from Bangladesh

Abstract:

Working Paper 16December 2015

Simone Dietrich, Minhaj Mahmud, Matthew S. Winters

Foreign aid donors try to make themselves visible as the funders of development projects in order to improve citizen attitudes abroad. Do target populations receive these political communications in the intended fashion, and do they succeed in changing attitudes? Despite the widespread use of the practice, there exists little evidence about the effectiveness of this strategy. We embed an informational experiment about a U.S.-funded health project in a nationwide survey in Bangladesh. Although we find limited recognition of the USAID brand, explicit information about U.S. funding slightly improves general perceptions of the United States. It does not, however, change respondent’s opinions on substantive foreign policy issues. We also find, contrary to existing arguments that foreign aid undermines domestic government legitimacy, that the information increases confidence in local authorities. These results strengthen our understanding of the efficacy of promoting donor visibility and shed light on an important debate in the area of governance that assesses the effect of external actors on government legitimacy.

Keywords: Foreign aid, aid branding, information experiment, Bangladesh

Working Paper 16December 2015

Foreign Aid, Foreign Policy, and Domestic Government Legitimacy: Experimental Evidence from Bangladesh

Simone DietrichUniversity of EssexCorrespondence to: simone.dietrich@essex.ac.uk Simone Dietrich is Senior Lecturer of Government at the University of Essex. She is also Director of ESSEXLab, the University’s social science experimental lab. Prior to joining Essex, Simone was Assistant Professor of Political Science at the University of Missouri, Columbia. Her research is published or forthcoming in International Organization, Journal of Politics, International Studies Quarterly, Journal of Experimental Political Science, World Development, European Journal of Development Research, and Oxford University Press.

Minhaj MahmudBRAC Institute for Governance and DevelopmentBRAC UniversityCorrespondence to: minhajmahmud@gmail.comMinhaj Mahmud is currently head of research of BRAC Institute of Governance and Development, BRAC University. Previously he has held various academic and research positions at Queens University Belfast and Keele University in UK, Bangladesh Institute of Development Studies (BIDS) and Jahangirnagar University in Bangladesh. He has completed his Ph.D. in Economics from the University of Gothenburg. His research broadly focuses in behavioral economics, experimental economics and development issues. His research is published in journals such as Economics Letters, Journal of Economic Behavior and Organization, Economica and Health Economics. He is currently a Visiting Scholar at the JICA Research Institute, Tokyo.

Matthew S. WintersUniversity of IllinoisCorrespondence to: mwinters@illinois.eduMatthew S. Winters is Associate Professor in the Department of Political Science at the University of Illinois at Urbana-Champaign. He received his Ph.D. in Political Science from Columbia University and was a Postdoctoral Research Fellow at the Niehaus Center for Globalization and Governance at Princeton University. His research interests include the political economy of foreign aid, corruption, and the processing of political information. He has published articles in Comparative Politics, International Studies Quarterly, World Development, and World Politics, among other outlets.

Working Paper 16December 2015

info@aiddata.orgwww.aiddata.org

AidData: AidData – a joint venture of the College of William and Mary, Development Gateway and Brigham Young University – is a research and innovation lab that seeks to make development finance more transparent, accountable, and effective. Users can track over $40 trillion in funding for development including remittances, foreign direct investment, aid, and most recently US private foundation flows all on a publicly accessible data portal on AidData.org. AidData’s work is made possible through funding from and partnerships with USAID, the World Bank, the Asian Development Bank, the African Development Bank, the Islamic Development Bank, the Open Aid Partnership, DFATD, the Hewlett Foundation, the Gates Foundation, Humanity United, and 20+ finance and planning ministries in Asia, Africa, and Latin America.

Acknowledgments: Versions of this paper have previously been presented at the 2014 International Political Economy Society Annual Meeting; the Brown University Conference on Accountability in Low and Middle Income Democracies; the SFB 700 and AidData Workshop on Foreign Aid and Effective and Legitimate Governance in Areas of Limited Statehood; the 2015 International Studies Association, European Political Science Association, and American Political Science Association annual meetings; the ISA and AidData Workshop on Foreign Aid and Government Legitimacy at Georgetown University; the 2015 Center for Advanced Studies Spring Symposium at the University of Illinois; the Niehaus Center for Globalization and Governance Conference on The Politics of Government Revenue at Princeton University; Northwestern University; the University of Pittsburgh; and Yale University. Thanks to David Bearce; Rob Blair, Ceyda Erten, Raymond Hicks, Susan Hyde, Nolan McCarty; Helen Milner, Laura Paler, Steven Radelet, and Kelly Senters for excellent discussions or comments on previous versions. Thanks to Saumil Dharia for excellent research assistance. We are grateful for financial support from the University of Missouri Research Board and the International Studies Association.

 

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Contents

Abstract ........................................................................................................................................................ 1

1. Introduction .............................................................................................................................................. 5

2. How Does Information about Foreign Aid Change Citizen Attitudes? ...................................................... 8

3. Potential Externalities of Information about Foreign Aid on Domestic Government Legitimacy ............ 10

4. Research Design .................................................................................................................................... 11

4.1 Experimental Interventions .............................................................................................................. 11

5. Outcomes ............................................................................................................................................... 13

6. Results .................................................................................................................................................. 14

6.1 H1: Brands and Logos Spread Information about Donor Funding .................................................. 15

6.2 H2: Effects of Information about Foreign Aid on Attitudes toward the Donor .................................. 17

6.3 H3: Effects of Information about Foreign Aid on Domestic Government Legitimacy ...................... 20

7. Conclusions ........................................................................................................................................... 23

Tables and Figures .................................................................................................................................... 26

Appendix .................................................................................................................................................... 33

References ................................................................................................................................................. 43

 

 

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1. Introduction

“[T]he branding … absolutely had a major impact on their perception of the U.S. I think our new global

branding is a major foreign policy achievement.” – Bill Frei, USAID (ND: 3)

In the introduction to their book on anti-Americanism, Katzenstein and Keohane (2007) outline three

reasons why improving attitudes toward the United States around the world is fundamental to U.S. foreign

policy goals. First, in the age of global terrorism, negative opinions toward the United States help to

create “breeding grounds for terrorism” in which terrorist organizations can recruit activists and draw on

the resources of sympathizers. Since there may be direct security consequences of high levels of

negative affect toward the United States, U.S. policymakers have an interest in cultivating positive

sentiment among foreign populations. Second, the general character of public opinion is relevant for

international cooperation, an ever more pressing objective in the era of climate change, the globalization

of disease, and other transnational threats. Positive public attitudes toward potential international

partners may be an important facilitator of interstate cooperation. Third and relatedly, the least expensive

power to exercise is “soft power,” the power that relies on others wanting what you want and trusting in

your judgment (Nye 2002). Where positive affect toward the United States exists, the United States will

be able to achieve diplomatic and security outcomes at lower cost.

When giving foreign aid, donor governments conduct public diplomacy by directly communicating their

financing role to intended beneficiaries abroad. An example can be found in Bagerhat District,

Bangladesh, where there is a large sign on the side of a newly built school building. The sign depicts two

women building a cyclone shelter along the side of a mangrove tree. Clockwise from the upper-right-hand

corner of the sign, an observer can find logos for three different nonprofit development organizations

headquartered in the United States or the United Kingdom (ACDI/VOCA, PCI, and MuslimAid) and for the

U.S. Agency for International Development (USAID), the overall funder of the project. This type of

``branding” is the most common way by which donors spread information to populations in developing

countries about their role in funding public goods and social services.

This type of public diplomacy has a long history in the United States as being a means for promoting the

country’s soft power by influencing public opinion. Nye (2008: 97, citing Pellis 1997: 33) notes that the

Roosevelt administration viewed America’s security to depend “on its ability to speak to and win the

support of people in other countries.” Such public diplomacy has played a central role in foreign aid policy

since the Foreign Assistance Act of 1961, which has required that U.S. overseas projects be marked as

“American Aid” in order to win the loyalties of local populations in the developing world. This effort at

 

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spreading information about U.S. development efforts was redoubled as part of the post-9/11 National

Security Strategy. According to Andrew Wilder (2010), U.S. foreign aid in Afghanistan, Pakistan, and Iraq

has been used as a ‘weapons system’ to promote stabilization and counterinsurgency objectives” (407).1

By communicating the origin of sponsorship to local populations, the U.S. government seeks to establish

positive associations with and affect toward the United States among people in need. Other donor

countries, such as the United Kingdom, have followed suit with their own aggressive branding policies.

Despite these prominent efforts by powerful countries to change attitudes around the world, little scholarly

attention has been paid to the effectiveness of public diplomacy in changing opinions and influencing

policy abroad from the grassroots level.2 What is more, little attention has been paid to the potential

unintended consequences that may arise from public diplomacy efforts and that may ultimately undermine

the strategic objectives of the United States or other donor countries. For instance, there is a prominent

theoretical argument that information about the external funding of development interventions will

undermine citizen support for their own governments where citizens expect the state to provide goods and

services (Bratton 1989; Fowler 1991; Whaites 1998; Gubser 2002; Brass 2010). Such a disconnect

between citizens and the state could lead to lower levels of government stability and reduce the quality of

governance in a state in the long run (Brautigam 2000; Knack 2001; Hoffman and Gibson 2005).3

To date, little rigorous evidence exists about the consequences of spreading information about foreign

development financing on peoples’ attitudes towards the donor. Nor do we know whether the most

commonly used strategy for spreading such information – the branding of development interventions

through the use of an aid agency logo – even effectively communicates that a foreign donor has

sponsored a given intervention.4 In their publicity materials, USAID cites macro-level observational data

on changed attitudes toward the United States in Indonesia following provision of humanitarian relief in

the wake of the December 2004 Indian Ocean tsunami.5 But the persistence of strong anti-American

                                                                                                                         1 According to Wilder (2010), foreign aid has been codified in U.S. Army counterinsurgency policy, which now includes non-military public affairs operations like “publicizing the opening of a water treatment facility” because such public goods, when labeled as being from the U.S. government, are thought to facilitate the overarching objective of winning the support of citizens away from insurgents and in favor of the foreign military and the government. Berman, Shapiro, and Felter (2011) use panel data to show that reconstruction spending in Iraq reduced insurgent violence. See also Lyall and Wilson (2009). 2 Existing scholarship largely focuses on government-to-government influence, where foreign aid is leveraged to convince aid-receiving governments to make policy concessions or promote democratic change at home. Recent examples in this tradition include Steinwand (2015, 2014), Dietrich and Wright (2015), Vreeland and Dreher (2014), Swedlund (2014), Bearce (2013), Bearce and Tirone (2010), and Flores and Nooruddin (2009). 3 Other scholars, however, have downplayed this alleged risk to state legitimacy and stability posed by foreign aid. Successful management of foreign aid relationships may actually legitimate the aid-receiving state when citizens observe representatives of the government working with non-state actors in a collaborative fashion (Tendler 1997). 4 Beyond citizens in developing countries, branding may also target donor publics and donor legislatures, serving as an important tool to communicate to domestic audiences what foreign aid efforts accomplish. Branding increases transparency by providing visual evidence of specific development contributions (Shah 2010) 5 Bill Frei, the Director of USAID’s Indonesia program at the time, argued, “The people of [Aceh] (Indonesia) saw the branding; they knew right away the U.S. government was responding. That absolutely had a major impact on their perception of the U.S. Without the branding, USAID would have been just one of 550 NGOs working in the area” (USAID ND: 3). See also http://www.usaid.gov/branding (accessed 26 May 2015).

 

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sentiment in countries to which the United States sends significant amounts of assistance raises

questions about whether information about U.S. development activity is having the intended effect, as do

not-infrequent official requests for USAID to avoid branding its interventions (Adelman 2011; Merkovic-

Orenstein 2011; Reid-Henry 2011; Birdsall, Vaishnav, and Cutherell 2012).

Using an original informational experiment6 in Bangladesh that includes a number of innovative outcome

measures, we shed light on three important questions: Do conventional foreign aid branding techniques

effectively communicate that a project was funded by a foreign country? Does information about donor

sponsorship improve attitudes toward the donor? Does this information undermine the legitimacy of the

aid-receiving government in the eyes of its own citizens? We also explore whether the effects of

information about foreign aid vary across different types of people (e.g., those who have had greater

previous exposure to the project; those who are more politically sophisticated). We research these

questions with reference to a prominent network of health clinics in Bangladesh – the Smiling Sun Clinics

– that are partially funded by USAID. A positive example of what foreign aid can do, these clinics

generally are regarded as quite successful in delivering healthcare to the population that they serve.7

Bangladesh serves as a useful testing ground for studying the effects of information about foreign aid. At

the level of government-to-government relations, Bangladesh has long served the United States as an

important partner in its global fight against terror (Congressional Research Service, 2010). The country is

the third largest Asian recipient of U.S. assistance after Afghanistan and Pakistan (State Department

2015)8, and U.S. government officials have emphasized the dual role that foreign aid can play in

improving the country’s counterterrorism capacity while also promoting good relations between the two

countries (Biswal 2014).

These positive government-to-government relations, however, mask growing anti-Westernism among the

population. In 2015, Italian and Japanese aid workers were killed in the country, and a string of attacks

against secular bloggers and publishers left more than a half-dozen Bangladeshis dead (Anam 2015).9

Despite the long history of good relations between Bangladesh and the United States and while the

country is not known for anti-American demonstrations of the type found in Iran or Pakistan, there

nonetheless appears to be a role for public diplomacy to play in Bangladesh as a relevant tool for

reducing anti-Western sentiment.

                                                                                                                         6 Recent papers by Nooruddin (2014) and Hyde (2015) highlight the potential of survey experiments for studying (sensitive) questions in international relations and comparative politics. 7 See Lance, Angeles, and Kamal (2012) for an impact evaluation of the clinics. 8 From all bilateral and multilateral donors, Bangladesh received US$17 per capita in foreign development assistance in 2014; this amount has been steadily increasing over the last 10 years (World Development Indicators 2014). 9 For more on attacks against Western aid workers, see Narang (2013).

 

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We find that the conventional U.S. practice of marking aid projects with the USAID logo is relatively

ineffective at revealing information about U.S. sponsorship. Once the role of the United States as financier

is made explicit, however, Bangladeshis who receive the information are more likely to view the U.S. in a

positive light as compared to other citizens who do not receive the information about U.S. sponsorship.

We find that treatment effects are heterogeneous and vary by population sub-groups. We find less

evidence that the information changes any specific policy attitudes. These findings suggest both that

public diplomacy through foreign aid is possible but also that more thought may need to be put into how to

spread relevant information.

In what many will regard as a surprising finding, we find that – rather than delegitimizing the domestic

government – information about donor financing improves our respondents’ views of their local

governments. In other words, citizens reward their own governments for bringing in external funding for

development interventions. Again, we identify important heterogeneous treatment effects across sub-

groups. Our findings directly contribute to an emerging debate among comparative politics and

development scholars about the effects of foreign aid on governance, as well as to our general

understanding of how citizens perceive the quality of governance and give credit to different levels of

government.

2. How Does Information about Foreign Aid Change Citizen Attitudes?

Despite donor governments’ assertions that information about foreign aid sponsorship can improve the

attitudes of citizens in aid-receiving states toward the donor, to the best of our knowledge, no foreign aid

agency has ever fully outlined a relevant theory of change. We understand the logic underlying the

potential benefits of information about incoming foreign aid flows to originate in the notion that

development interventions are likely to be perceived as helpful and beneficial by local populations. The

spread of information about the sources of funding therefore should lead to the transfer of positive affect

about the development intervention itself to the donor country that has funded it.

If the mechanism of information transmission is the branding of foreign aid with the donor agency logo –

as it most commonly is – we must assume that people associate the donor brand (e.g., the USAID logo)

with the donor government. This assumption seems to have been treated relatively uncritically in the

development world. Given low levels of literacy and education, it is far from certain that people who see a

donor logo will necessarily recognize that it is associated with a far-away country or – even more to the

point – that it is advertising foreign sponsorship of an aid project. That is, the target audience of foreign

 

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aid branding may not actually infer that the logo has been placed on a development project to indicate

financial sponsorship or the transfer of resources. In the empirical section below, we test the initial

hypothesis that donor branding increases citizens’ awareness of foreign sponsorship of a development

intervention (H1). Our results show that this hypothesis is confirmed for only a limited proportion of

respondents.

We subsequently test the main hypothesis related to the effects of information about foreign aid. When

explicitly informed about the aid sponsor,10 citizens may update their opinions of and attitudes about the

donor in a positive fashion through either conscious or subconscious processing of the informational

treatment. At a conscious level, we can imagine citizens reflecting on the fact that a foreign donor has

provided an intervention in a way that generates cognized feelings of gratitude that lead to more positive

affect toward the donor. At a subconscious level, it is possible that positive attitudes about or valuations

of a development intervention are subconsciously transferred to the project sponsor through the process

that Lodge and Taber (2013) refer to as “hot cognition.” Research on “affect heuristics” suggests that

current emotion influences decisions (e.g., Slovic and Peters 2006; Wilson and Arvai 2006), such that

positive emotions generated by information about foreign sponsorship of a development intervention

should then make people more likely to judge the U.S. in a more positive light and to be more supportive

of U.S. policies (H2).11

Previous results in the literature are mixed. Using macro-level data, Goldsmith, Horiuchi, and Wood

(2014) show that variation in levels of funding from the United States’ President’s Emergency Plan for

AIDS Relief (PEPFAR) positively predict attitudes toward the United States across about 50 PEPFAR-

receiving countries. Andrabi and Das (2010) show that attitudes toward foreigners vary based on

proximity to the fault line of a 2005 earthquake in northern Pakistan that brought a steady flow of

humanitarian aid to the country. A study of foreign assistance in northeast Afghanistan over the period

2007-2009 reveals that foreign aid “did not have an impact on attitudes toward foreign forces” (Zürcher,

Koehler, and Böhnke 2010, 5), while qualitative work from Afghanistan reports that “aid and development

projects were consistently negatively described by Afghans” (Fishstein and Wilder 2012, 41). In survey

data collected from Indian respondents, Dietrich and Winters (2015) find a small but not statistically

significant effect of information about U.S. sponsorship of a foreign aid project in the health sector on

respondents’ perceptions of U.S. favorability.

                                                                                                                         10 In the treatment that we employ in our experiment, we directly provide information about sponsorship after exposing respondents to the brand and asking them an initial follow-up question. This allows us to investigate whether the brand is conveying the intended information and then investigate the effects of the actual information that donor brands are intended to convey. 11 Other affective associations are possible. If the reaction to a development intervention is negative (e.g., because of poor project implementation or cultural inappropriateness), this affect may be consciously or subconsciously transferred to the foreign donor.

 

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3. Potential Externalities of Information about Foreign Aid on Domestic Government Legitimacy

The distribution of information about external funding of development projects may also influence citizens’

views of their own government.12 Some have suggested that widespread information about the presence

of donors in various social and economic sectors may signal a lack of competence or willingness on the

part of the government to provide basic goods and services, rendering governments unable to make

legitimate demands of their citizens (Bratton 1989; Fowler 1991; Whaites 1998; Gubser 2002; Brass

2010). For instance, external funding of basic goods and services may undermine the “fiscal contract”

between the state and its citizens, lowering the tax morale of citizens. This potential disconnect may

ultimately contribute to a pattern in which foreign aid flows undermine the quality of governance in a state

(Brautigam 2000; Knack 2001; Hoffman and Gibson 2005). Insofar as foreign aid is given for the purpose

of supporting allied governments, this undermining of an ally’s domestic legitimacy might overshadow any

benefits of improved perceptions of the donor brought about by the diffusion of funding information.

On the other hand, successful management of relationships with foreign aid donors may actually help

legitimize the state and its institutions (Tendler 1997). Since many donor-funded development

interventions directly target some local communities but not others and since project implementation

usually requires the consent and cooperation of local authorities, citizens might directly credit their local

authorities for bringing a project into their community. Indeed, the presence of externally-funded projects

can serve as signals of the competence of a local government in providing goods and services to its

community (Cruz and Schneider forthcoming; Guiteras and Mobarak 2014). If citizens perceive local

leaders as playing a role in the project negotiation process it is plausible that leaders will receive credit for

securing the project for the local area. What is more, local authorities often run on election platforms

where they credit themselves with the promotion of local development.

We therefore have two competing hypotheses. It may be that information about donor sponsorship of

development interventions undermines domestic government legitimacy (H3a). Alternatively, it may be

that information about donor sponsorship of development interventions improves citizen perceptions of

their domestic government (H3b).

So far the literature has found little evidence of a negative externality and more often has estimated a

positive relationship between non-state service provision and attitudes toward the state. Using data from

                                                                                                                         12 Increasingly, scholarship is conducting individual-level analyses to understand the influence of external actors on peoples’ perceptions of their domestic institutions. Examples include Bakke (2015) and Blair (2015). Carnegie and Dolan (2015) propose that some governments will refuse foreign aid in order to protect their public image.

 

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the Afrobarometer surveys, Sacks (2012) finds a positive correlation between non-state service provision

and tax morale (i.e., citizens’ willingness to recognize the state’s legitimate right to collect taxes). This is

also the finding of Zürcher, Köhler, and Böhnke (2010) in northeastern Afghanistan: in places with higher

levels of foreign-funded small infrastructure projects, levels of support for the district and provincial

government were higher, although this effect did not persist across the four years of the study (see also

Böhnke and Zürcher 2013). Guiteras and Mobarak (2014) show in a field experiment that subsidies for

small-scale infrastructure in rural Bangladesh initially increases citizen support for the local government

until people are given information that the subsidies came from an NGO and that the local government

played no role in obtaining them. Cruz and Schneider (2014) show how local government officials in the

Philippines reap electoral benefits from the presence of a World Bank-funded and central government-

implemented development project. In survey experimental data from India, Dietrich and Winters (2015)

find no evidence that favorability ratings for domestic government officials or institutions decrease when

individuals receive information about the foreign funding of a development project.

4. Research Design

To test our hypotheses, we ran an informational experiment, embedded in a nationally representative

household survey of Bangladeshi citizens. Between 7 September and 1 October 2014, enumerators from

the BRAC Institute of Governance and Development conducted face-to-face interviews with 2,294

respondents from 55 subdistricts (upazila) spread across all seven divisions of Bangladesh.13 During the

survey, we provided information about a development intervention to both our treatment and control

group; the treatment group received additional information about U.S. sponsorship of the intervention.

The information that we provided in the survey closely approximates the kind of information that citizens

typically would receive about donor sponsorship (e.g., through the media or through conversations in their

communities).

4.1 Experimental Interventions

After two initial questions about what policy issues were of importance to the respondents and the current

state of affairs in Bangladesh, we assigned randomly sampled respondents to one of the two versions of

the survey.14 Both versions of the survey described and showed a brief video related to the Smiling Sun

Clinics, a network of non-profit health clinics catering to poor communities and partially funded by USAID

                                                                                                                         13 The sampling strategy is described in the Appendix. 14 In Table A1 in the Appendix, we show that our treatment and control groups are balanced on a number of background covariates, including previous use of and awareness of the Smiling Sun Clinics that we use in our intervention.

 

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since 1997. Before showing respondents the video, we asked them if they were familiar with the Smiling

Sun Clinics and if they, a family member, or a close friend had ever been to a Smiling Sun Clinic. The

video was one-minute long and dramatized the way in which the pre-natal services provided by the clinics

calmed the anxieties of two expecting parents. It was shown to respondents on the hand-held tablets on

which enumerators conducted the survey. In the control version of the survey, the video was branded at

the bottom of the screen with the Smiling Sun logo and name. In the treatment version of the survey, the

video was branded with the USAID/Bangladesh logo and the tagline (in Bangla) “On behalf of the

American People.” Figure 1 provides screen shots from two difference scenes in the video for the

treatment and control groups; except for the branding, the video was identical in the two conditions.

Immediately following the video, we asked respondents in both conditions an open-ended question about

where “most of the financial assistance for the Smiling Sun Clinics” has come from. We use this question

to examine the first hypothesis that donor branding signals the identity of the foreign sponsor. To

examine the lasting impression left by the donor brand, we also asked our respondents toward the end of

the survey if they could describe the logo that they had seen in the video.

In the treatment condition only, we provided direct information that the Smiling Sun Clinics are funded by

a foreign donor, informing respondents:

Since 2007, the United States has been giving assistance to build Smiling Sun Clinics in

Bangladesh. The funds have been provided through USAID. The United States has

provided more than 4,500 million taka15 in support of the Smiling Sun Clinics.

In this condition only, we then asked our respondents if they knew why the United States was providing

financial assistance to Bangladesh and also if it was good for Bangladesh to take money from the United

States for the Smiling Sun Clinics and why. In addition to collecting useful information, these questions

were meant to strengthen the treatment, increasing the probability that respondents both absorb the

factual information that the United States had provided the funding and also think through the relationship

between the United States and Bangladesh in a way that would activate their own sentiments about the

donor-recipient relationship.

                                                                                                                         15 This is about US$57 million.

 

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5. Outcomes

The remainder of the survey included outcome measures related to Bangladeshi relations with the United

States, confidence in Bangladeshi government institutions, and the quality of the Smiling Sun Clinics. We

use a mixture of questions to get at each of these topics. One of the contributions of our survey is that, in

addition to general measures of abstract attitudes toward the foreign aid donor, we use policy-specific

measures and a set of questions that judge whether affect toward the donor might spill over into attitudes

toward its commercial goods.

To measure how the treatment affects citizens’ attitudes toward the donor, we ask a straightforward

question about the influence of foreign countries on Bangladesh. Respondents were first asked about

whether the United States had “a lot” of influence on Bangladesh or “not much” influence on Bangladesh.

We asked about the United States as one of a set of ten countries. For respondents who said that the

United States had a lot of influence, we subsequently asked whether this influence was good or bad.

In addition to this general question, we asked policy-specific questions in the realm of trade and security.

If information about foreign aid is to work as public diplomacy, any warm glow that it generates needs to

also lead citizens exposed to the information to update their policy preferences on issues relevant to the

donor. For trade, we generally described U.S.-Bangladeshi trading relations and then asked whether

respondents thought that Bangladesh should give more priority to continuing trade with the United States

or rather to diversifying its trading partners. For security, we described Bangladesh’s frequent

participation in U.N. Peacekeeping Operations and then asked respondents which of nine countries they

would most like to see in command of Bangladeshi troops.

By asking questions about specific policies, we increase the costs of showing favoritism toward the United

States. Answering the influence question is cheap and given the nature of the treatment, the “right”

answer might be obvious. Taking a stand on trade policy or attitudes toward U.N. Peacekeeping

Operations, however, involves expressing favoritism toward the United States in an arena where the

respondent may have different initial instincts.

Beyond these questions about Bangladesh’s foreign policy, we asked two types of questions about

international commerce, looking to see if positive attitudes generated by information about foreign aid spill

over into the consumer preferences of citizens in aid-receiving countries. Existing evidence on product

marketing shows that overall beliefs and judgments about a foreign country influence individuals’

perceptions and judgments of consumer products associated with that country (Cattin, Jolibert, and

 

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Lohnes 1982; Peabody 1985; Han and Terpstra 1988). If information about foreign aid is successful at

improving people’s attitudes toward the donor, this should be visible in people’s attitudes toward things

associated with the donor, such as multinational corporations headquartered in the donor country or

goods produced in the donor country.

First, we asked respondents to express their level of trust in seven different multinational corporations,

two of which are U.S.-based corporations (and were identified as such in the question prompt): Apple and

Coca-Cola. Second, we asked respondents about six different types of consumer goods and asked them

to choose which country (from a list of nine) produced the highest quality products. If positive affect is

being generally transferred to the donor country, we should see that transfer reflected in respondents’

perceptions of American companies and products.

To measure domestic government legitimacy, we asked a series of nine questions about institutions in

Bangladesh ranging from the “local government” and the “national government” to “NGOs” and “banks

and financial institutions.” For each institution, we asked respondents to say whether they had full, partial,

or no confidence in the institutions and the people leading them. We study some of these questions

individually and also an index of responses to the questions about government institutions. To parallel

the key outcome variable in Sacks (2012), we also included a tax morale question on the survey, asking

respondents to agree or disagree with the statement that “the tax department of the government has the

right to make people pay taxes.” And finally we asked respondents about levels of corruption in the

government to see if information about U.S. involvement would lead people to perceive their government

as more corrupt because it did not itself implement the project or whether U.S. involvement would

encourage people to perceive their government as less corrupt since foreign donors were willing to

operate in the country.

6. Results

We draw our inferences about the way in which donor visibility affects people’s attitudes by looking for

differential responses across these variables between the group that was told about and asked to

consider the fact that the United States funds the Smiling Sun Clinics and the group that was not exposed

to information about U.S. funding of the clinics. Throughout the paper, we report results from difference-

in-means tests and Wilcoxon rank-sum tests. In Table A2 in the Appendix we present results from logistic

and ordered logistic regression models that control for a host of background covariates. These results are

almost identical in terms of magnitude and statistical significance to those presented below. We further

examine heterogeneous treatment effects across certain sub-groups in the population. For example, we

 

15  

provide sub-group analyses related to levels of education and political involvement as we expect these

variables to influence the treatment effects. We also distinguish among respondents who were previously

familiar with the Smiling Sun Clinics (and thus more likely to have been previously exposed to USAID

branding) and those who were not familiar with the intervention.

6.1 H1: Brands and Logos Spread Information about Donor Funding

Donor governments commonly rely on visual logos to spread information about donor financing. The U.S.

government uses the USAID brand to communicate its financial assistance to beneficiaries abroad. In this

section, we provide evidence that surprisingly few respondents in the treatment condition were able to

correctly infer the meaning of the USAID brand. We find that that the treatment effects are larger among

respondents with previous exposure to the clinics or higher levels of education.

In both the treatment and control conditions, respondents were asked, immediately after watching the

video and before any additional information, “Do you know from where the Smiling Sun Clinics get most of

their financial assistance?” Despite the fact that the treatment condition used a video where the USAID

logo and the statement “On behalf of the American people” (in Bangla) were present across the bottom of

the video for the duration of the video, the modal answer in both the treatment and control conditions, as

can be seen in Table 1, was for the respondent to say that he or she did not know the source of funding

for the Smiling Sun Clinics.

Although the effect size is smaller than we expected a priori, the USAID branding in the treatment version

of the video alerted at least some of the respondents to the fact that the United States was funding the

intervention. The proportion of respondents saying that the Smiling Sun Clinics were funded by the United

States increased by 13 percentage points between the control and treatment conditions. Most of the

movement came from people who otherwise would have said that they did not know. An additional three-

to-four percent of respondents in both conditions said that the funding came from “foreign aid” or “foreign

countries.”

The remainder of Table 1 shows the patterns in the data for several select subgroups. We find larger

effects among people who have previously used or were previously aware of the clinics. These are

respondents who we can think of as having had multiple exposures to the treatment. Indeed, looking only

at respondents in the control group, among those who say that they had previously used the Smiling Sun

Clinics, 26 percent were able to correctly identify the United States as the funder, which is statistically

significantly different from the 8 percent of non-previous users in the control group who are able to do so

 

16  

(p < 0.01).16 Among previous users, the stimulus that we provided in the treatment condition increases

the proportion that cites the United States as the sponsor of the clinics by 14 percentage points (p < 0.01).

Among those who were previously aware of the clinics (but had not personally used them or had family or

friends use them), only 16 percent of the control condition respondents were correctly able to identify the

United States as the funder, a proportion that increased by 18 percentage points in the treatment

condition (p < 0.01). These effects suggest that donor brands will be most effective at informing or

reminding people of donor financing when those people have a pre-existing awareness of or connection

to that intervention.

We also looked at highly educated respondents (i.e., respondents with a completed secondary education

or above). We find the largest effect of the branding treatment among this group: a 23 percentage point

increase over a relatively high baseline of 21 percent naming the United States in the control condition.

As one might expect, more highly educated respondents seem the most capable of fully absorbing the

meaning of a brand – perhaps simply because their literacy allowed them to read the “On behalf of the

American people” tagline – and drawing a correct inference about its relationship to financial sponsorship.

Nonetheless, 47 percent of our highly educated respondents in the treatment group still said that they did

not know from where the Smiling Sun Clinics had received their funding.

Toward the end of the survey, we reminded respondents that there had been a small logo in the video

that they had watched earlier, and asked them if they could tell us what that logo had indicated. In the

treatment condition, five percent of respondents mentioned USAID in their answer, while less than one

percent of respondents in the control condition did. In both conditions, the vast majority of people

responded to the question by talking about the Smiling Sun Clinics in general. Often, they described the

video in general and not the logo per se. In both conditions, 27 percent of respondents said that they did

not remember what had been in the video or did not know how to describe the logo that they had seen.

This is an additional piece of evidence that, insofar as the USAID logo was observed in the treatment, it

did not register with our respondents in a profound and lasting way.

These results suggest that, overall, the USAID brand is not well known in Bangladesh or is not

understood as a signal of U.S. financing. This impression was confirmed by author field visits to Smiling

Sun Clinic locations where people nearby were unable to identify the funder or to say what the USAID

logo on the clinic meant. After the tagline was read to these individuals, they were able to say that money

for the clinics must have been coming from the American people.

                                                                                                                         16 The proportion of previous clinic users in the control group who correctly identify the United States as the funder of the clinics is, in fact, larger than the proportion of non-previous users in the treatment group who do so (δ = 0.06; p < 0.13). In Appendix Table A2, we provide a logistic regression analysis of correct answers to the funding question among respondents in the control group.

 

17  

6.2 H2: Effects of Information about Foreign Aid on Attitudes toward the Donor

To test if information about U.S. sponsorship of the Smiling Sun Clinics changes Bangladeshi opinions

about the United States, we now proceed to evaluate the combined effects of the branding and the

second-stage of our treatment where we directly informed respondents in the treatment condition that the

U.S. government has been funding the Smiling Sun Clinics for almost 20 years (after having asked the

questions above where we tried to assess whether they understood the meaning of the USAID logo).

Although we did not ask respondents an additional question to confirm that they understood the explicit

information about the history of U.S. funding for the Smiling Sun Clinics, we asked respondents later in

the survey to identify which of seven foreign donor countries or international financial institutions it would

be best for Bangladesh to ask to financially support the Smiling Sun Clinics in the future. Among those

who had a preference, 65 percent in the treatment condition said the United States, while only 52 percent

named the United States in the control condition, a difference that is statistically significant (p < 0.01).17

This suggests that a subset of respondents in the treatment condition were absorbing the information,

although the magnitude of the change is still smaller than we would have expected ex ante.18 We also

tried to reinforce the information by asking respondents why they thought the U.S. provided such funding

and whether or not they thought that it was good that the U.S. provided such funding.19

We present intent-to-treat (ITT) effects that are unbiased estimates calculated by taking the difference

between average outcomes in the treatment and control groups. In the appendix, we also estimate

complier average causal effects (CACEs) following the instrumental variable framework suggested by

Gerber and Green (2012) using a set of assumptions about non-compliance in the treatment group. While

we have a measure of non-compliance for respondents in the control condition (i.e., 11 percent of

respondents say that the U.S. financed the Smiling Sun Clinics without having been explicitly told so), we

do not have a measure that captures non-compliance among the treated (i.e., the proportion of

respondents who did not understand or chose not to believe the direct information about U.S. financing).

In the appendix, we make assumptions about non-compliance in the treatment condition varying from ten

                                                                                                                         17 In the control condition, 24 percent of respondents did not supply an answer, while only 17 percent of respondents in the treatment condition declined to name a preferred donor. 18 The answer to this question, however, might be influenced by other factors besides the treatment (e.g., a preference for a foreign country other than the United States). 19 For both of these questions, respondents almost universally assigned earnest development motives to both the United States and Bangladesh. Slightly more than half of respondents in the treatment condition provided their thoughts on why the United States supplied funds. With the exception of one respondent who said that the United States provided funds “for [its] own benefit,” all other respondents who provided an answer described the United States as wanting to provide health care services or otherwise encourage development in Bangladesh. Many more people provided a response as to why Bangladesh receives the foreign aid, and these were nearly all development-oriented reasons. Just three respondents took the opportunity to say that it was “not good” for Bangladesh to accept foreign money; one of these respondents related his opposition to his Islamic faith, while the other two did not say why they felt this way.

 

18  

to 25 percent. As is to be expected, these plausible CACE estimates are larger than the ITT estimates

that we present here.

To measure diffuse attitudes toward the United States, we told respondents that we wanted to know what

they thought “about the overall influence of some other countries on Bangladesh.” They first told us

whether they thought that a given country had a large influence or not, and among those who answered a

large influence, they told us whether it was positive or negative. The United States was one of 10

countries about which we asked. In both the treatment and control conditions, around 90 percent of

respondents said that the United States had a large influence. The proportion was slightly (and

statistically significantly) higher in the treatment condition (δ=0.03, p < 0.01). Among those who said that

the United States had a large influence, 95 percent of respondents said that this influence was positive. 20

In the first row of Table 2, we report a difference-in-means test and a rank-sum test on a three-category

variable about U.S. influence. Given that most respondents say that the United States is influential in a

positive way, the treatment effect is small, but it is positive and obtains conventional levels of statistical

significance. In Appendix Table A4, we show that the treatment effect is driven by movement from the

“small influence” category into the “large and positive influence” category; the proportion of people

believing that the U.S. has a “large and negative influence” on Bangladesh remains constant across the

two groups.

In Table 3, we present results for this outcome variable from two sets of subgroups. First, we compare

respondents who had previous knowledge about Smiling Sun Clinics to those who did not. Second, we

compare more highly educated respondents to their less educated counterparts. The results reveal that

the treatment effect that we observe in the overall population is particularly concentrated among those

respondents who were not previously aware of the Smiling Sun Clinics and among less educated

respondents. Since respondents who were not previously aware of the Smiling Sun Clinics were also less

likely to have been aware of U.S. sponsorship of the clinics, we likely are seeing more updating of

attitudes toward the United States among this group. Among less educated respondents, it appears that

we are seeing more updating of opinions about the United States because of more negative baseline

perceptions (as indicated by the average value taken by the influence variable in the control condition).

The subgroup treatment effects, however, are not statistically significantly different from one another in

either case.

                                                                                                                         20 These high levels of pro-U.S. sentiment are comparable to recent polling by Pew Research (2014). In the Pew data, 76 percent of Bangladeshi respondents said that they had favorable views of the United States. Among Asian countries, only respondents from the Philippines (92 percent), South Korea (82 percent), and Vietnam (76 percent) were either equally or more positively inclined towards the United States.

 

19  

When respondents were asked about their preferences on specific policy relevant to the United States,

our treatment elicited less movement in attitudes. As the second row of Table 2 shows, when asked

whether “Bangladesh should put more emphasis on trading with the United States or more emphasis on

diversifying its trading partners,” respondents in the treatment condition were slightly more likely to say

that Bangladesh should trade more with the United States, but the effect was not statistically significant.

When asked which country (from a list of nine) should provide the commander for Bangladeshi troops in

U.N. Peacekeeping Operations, very few respondents in either the treatment or control condition named

the United States as the preferred country; if anything, the proportion of respondents doing so decreased

between the control and treatment conditions.21

To measure the extent to which U.S. financing of development interventions might spill over into improved

commercial relations for U.S. companies, we gauged consumer sentiment in two ways. First, we showed

respondents logos for eight different global companies and asked respondents to rate the company on a

scale from one to seven with regard to how much they trusted the company. We included two U.S.

brands in this question: Coca-Cola and Apple. As the first two rows of Table 4 show, there were small

positive increases in the levels of trust for both companies, although only the increase for Apple, which

was greater than that for Coca-Cola and which had a lower baseline level of trust in the control condition,

was statistically significant.

We then named six different categories of consumer products and asked our respondents to name the

country that they thought did the best job of producing high quality products in that category. Overall, our

respondents did not regard the United States as a producer of high-quality consumer goods. Among

respondents who provided an answer for at least one of the categories of consumer goods, almost 70

percent did not mention the United States for any of the goods.22 The treatment appears to have had

some marginal influence here. The third row of Table 4 demonstrates that the average number of times

the United States was mentioned as a producer of high-quality consumer goods was higher in the

treatment condition, although the difference misses conventional levels of statistical significance (p <

0.14).

In sum, we find evidence that providing information about the foreign funding of development projects to

Bangladeshi citizens leads them to update their general attitudes toward the donor and also toward a

multinational corporation associated with the donor. It is, of course, worth highlighting that Bangladeshis

                                                                                                                         21 While 29 percent of respondents said that they did not know who should be in command of a U.N. Peacekeeping Operation, the clear favorite among those who answered the question was Malaysia, named by 57 percent of respondents who provided an answer. India and China were the second and third most common answers. 22 Of those that did mention the United States, most commonly they cited U.S. cars. India was highly regarded as a producer of motorcycles. China was the most highly regarded producer of televisions, refrigerators, mobile phones, and watches. Substantial numbers of people also mentioned Malaysia as a producer of high-quality televisions and refrigerators.

 

20  

already widely believe that the United States has a large and positive influence on their country, and so

the treatment results in some additional people saying that the United States has a large and positive

influence on Bangladesh. On specific foreign policy issues – increasing trade with the United States or

wanting Bangladeshi troops to serve under U.S. command – the treatment does not meaningfully change

Bangladeshi opinions. With regard to U.S. commercial brands, there is a slight effect of the treatment on

how people view Apple, although no effect on the already trusted Coca-Cola.

6.3 H3: Effects of Information about Foreign Aid on Domestic Government Legitimacy

In this section, we examine how our informational treatment affects the level of confidence that citizens

express in their domestic government institutions. We also look for effects of the informational treatment

on tax morale, an attitudinal outcome that has been studied elsewhere as an indicator of the strength of

the fiscal contract between a government and its citizens (Sacks 2012; Paler 2013). Again, we present

ITT effects below and CACE estimates for different levels of possible non-compliance among the treated

in Table A7 in the Appendix. As above, the CACE results show that the effect of our treatment on the

treated is larger than the ITT effects.

Our primary outcome variables are drawn from a series of questions about the respondent’s level of

confidence in Bangladeshi organizations and institutions. Respondents were asked whether they had “full

confidence, partial confidence, or hardly any confidence” in their local government, the national

government, and their village leader, alongside other institutions such as the military, NGOs, and political

parties. In addition to studying several of these individual questions, we create a scale that ranges from 0

to 14 based on the answer to seven of the questions (i.e., central government, local government, village

leader, political parties, judiciary, military, and police). We also asked respondents to indicate the degree

to which they agreed (on a five-point scale) with the following statement: “Those who govern the country

are engaged in rampant corruption.”

Before presenting our results, it is important to highlight that the level of confidence in the national

government is quite high in general: 50 percent of the respondents indicated that they had “full

confidence,” and 13 percent said that they had “partial confidence.” Citizens’ confidence in their local

governments is somewhat lower, with 34 percent saying that they had “full confidence” and 53 percent

indicating that they had “partial confidence.” Drawing on research that explores citizen interactions with

state institutions, we know that for the daily lives of ordinary people in developing countries the central

government and its institutions remain distant and abstract entities (e.g., Bratton 2010). Citizens are more

 

21  

likely to interact with and form political relationships with local authorities. We thus expect our treatment to

be more likely to influence citizen perceptions of their local but not necessarily their central government.

As Table 5 indicates, we find no initial evidence that our treatment undermines domestic government

legitimacy. Instead, we find evidence that our respondents express more positive opinions about their

own government in the treatment condition. Although there is no discernible difference between the

treatment and control conditions for confidence in the national government, we detect a statistically

significant increase in the levels of confidence in local government.23 We also find that treated

respondents indicate more confidence in the overall set of state institutions captured in our additive index;

this difference is also statistically significantly. Our treatment also caused our respondents to view

politicians as less corrupt than respondents from the control group; the difference between groups is

again statistically significant.

Through a series of subgroup analyses presented in Table 6, we identify three groups for whom the

information treatment has a particularly pronounced effect on their level of confidence in the local

government: (1) previous users of the Smiling Sun Clinics, (2) people who report being active in politics,

and (3) the highly educated (i.e., those with a completed secondary education or above). For each group,

there is a plausible explanation for why we see larger treatment effects linked to these groups viewing the

development intervention as beneficial for their community. Previous users, for instance, are particularly

likely to have positive perceptions of the clinics. If being exposed to information about the foreign funding

of these clinics leads these users to perceive that their local government played a role in arranging for the

clinics to be located in their area, they will be inclined to transfer positive affect to their local government.

For politically active and more educated respondents, we similarly might expect that these individuals

perceive the difficulties of securing foreign funding in a way that makes them likely to credit the local

government for securing the Smiling Sun Clinics for their locality. As requests for health clinics originate

from local governments that lack resources for service provision, more politically active and educated

respondents may view external provision as complementary to – and not in conflict with – service

provision by the state.

When comparing previous users of the Smiling Sun Clinics to non-users, the estimated treatment effect

with regard to confidence in local government is substantially larger among previous users of the clinics.

On a three-point scale, previous users express opinions of their local government that are 0.26 points

higher in the treatment group as compared to the control group. Among non-previous users, the control

                                                                                                                         23 Appendix Table A5 shows how this result is mostly driven by movement of people who answer “partial confidence” in the control condition into the “full confidence” category in the treatment condition.

 

22  

and treatment groups are indistinguishable. The different treatment effects within the two subgroups are

statistically significantly different from one another (p < 0.01).

Among respondents who consider themselves at least somewhat politically active, stark differences exist

between treatment and control conditions.24 Table 6 shows that, among politically active respondents,

our treatment increases confidence in local government by 0.22 points (p < 0.01), whereas among

respondents who do not consider themselves politically active, there is no significant difference between

the treatment and control groups in terms of confidence in the local government. Whereas our politically

active respondents have lower opinions of the local government in the control condition than non-

politically-active respondents, the treatment improves their opinions of local government to the extent that

they are higher than those of non-politically-active respondents in the treatment condition. The difference

between the treatment effects for the two subgroups is statistically significant.

Finally, we estimate conditional average treatment effects across levels of education. Among

respondents with secondary education and higher, the treatment improves attitudes toward the local

government by 0.16 points (p < 0.01). Among less educated respondents we do not observe a significant

improvement in attitudes. When comparing the treatment effects within these two subgroups, we find that

the difference falls below conventional levels of statistically significance (p < 0.14).

Overall, we find no evidence of a negative externality of foreign aid on domestic government legitimacy.

Instead, we find that information about the foreign sponsorship of a development intervention increases

some respondents’ confidence in their local governments. This effect is particularly concentrated in

previous users of the Smiling Sun Clinics, more politically active and higher educated respondents. It is

these subsets of people who recognize the value of the clinic and reward their local governments for

securing foreign funding for the clinics. What is more, respondents with higher levels of education and

political involvement are more likely to understand the process through which foreign aid projects are

distributed across localities and may thus be more likely to reward their local government for securing the

project.

   

                                                                                                                         24 Respondents could describe themselves as “not at all,” “somewhat,” or “very much” politically active.

 

23  

7. Conclusions

Using an informational experiment embedded in a nationally representative survey in Bangladesh, we ask

three questions about how information about the foreign sponsorship of development interventions might

or might not serve its public diplomacy role of changing opinions among the citizens of an aid-receiving

country. We look first to see if the widespread technique of branding foreign aid interventions with a

donor’s logo effectively communicate that a project was funded by a foreign country. Then we look to see

whether information about external funding improves attitudes toward the donor. And finally we examine

whether there is a positive or negative externality of this information on citizens’ views about their own

government.

We find that providing information about U.S. sponsorship of a popular health intervention has a small but

detectable effect on attitudes toward the donor. From a high baseline belief that the U.S. has a large,

positive influence on Bangladesh, we find an increase in positive perceptions of the United States among

our treatment group. We also find small increases in perceptions of one U.S.-based company. We do not

find an impact on attitudes related to specific foreign policies. The estimation of heterogeneous treatment

effects reveal that our treatment is especially likely to improve attitudes toward the United States among

respondents who had previous awareness of the Smiling Sun Clinics and have at least a completed

secondary education.

We find no evidence of an oft-cited potential negative externality of non-government service provision –

that it might undermine the legitimacy of the government (and thereby undermine overall strategic

objectives of the donor). Instead, we find evidence for the opposite hypothesis: donor visibility improves

attitudes toward the local government. This effect of our information treatment is particularly pronounced

among previous clinic users, the more politically active, and respondents with higher levels of education.

Perhaps the most surprising finding in the study is how limited the information transfer from donor

branding seems to be. Immediately after watching a video branded with a donor logo, only a minority of

our respondents are able to identify the United States as the funder of the intervention portrayed in that

video. Later in the survey, we find that few respondents remember the logo. Our findings suggest that the

U.S. government’s annual multi-million dollar investments in the marking of aid with the USAID logo may

not be accomplishing their intended public diplomatic purpose. Other donor governments around the

world, including Britain and Germany, have recently changed their branding strategy to feature national

flags in lieu of logos. Whether flags are more effective brands has yet to be systematically tested. Our

 

24  

results also suggest that donor governments may want to reconsider, in general, whether branding

through logos is a sufficiently important task in the pursuit of their national interest.

That previous users of the clinic are better able to make the association between the logo and U.S.

funding of the health clinics speaks to the ways in which repeated exposure to brands may matter. It is

among this group, as well, that we observe large effects of the treatment on attitudes toward the local

government, suggesting that information about foreign funding of development interventions will be most

salient for people who actually have had exposure to the intervention itself. That we do not see effects for

the foreign policy questions among this group also helps give us confidence in the meaningfulness of the

null effects that we observe there.

Our findings form the basis of future research. In the current survey, we have allowed respondents to

draw their own conclusions about the implications of foreign aid projects. In the real world, however,

people often rely on information sources to provide an interpretation of politically-relevant information.

That is, aid projects may be described positively or negatively by local elites and/or media. In a follow-up

survey we plan to impose information about project quality by designing survey vignettes that describe

U.S.-funded projects in positive and negative ways. If respondents are given information about project-

failure or corruption within a project, will this knowledge influence their attitudes toward the project

sponsor and their local government? Will a negative project description offset our observed positive

effects on respondent attitudes toward their local government?

Another interesting question to be addressed in future research would be to examine whether our current

project findings apply to domestic sectors beyond health. Since the project we study is specific to mothers

and newborn children and is highly valued across Bangladesh, the results may mask important

differences across sectors.25 Respondents may evaluate government responsibilities differently in the

security sector where the state is viewed as central to the provision of order. This would require that future

evaluations would either replicate our study in the security sector or employ a new research design that

accounts for potentially important heterogeneity across foreign aid projects.

Finally, our study motivates further research that investigates the effects of donor visibility in different

political contexts. In Bangladesh, as we have shown, respondents mostly hold favorable views towards

the United States even though there is a growing current of anti-Western sentiment in the country. Even in

the face of high baseline support for the U.S., the information treatment caused respondents to regard the

United States even more positively, though the extent to which attitudes could be changed was limited. In

                                                                                                                         25 In the appendix, we look for differential effects of the treatment on women and find that treatment effects are, if anything, smaller among this group that might have been more receptive to the information.      

 

25  

countries, like Egypt, Pakistan, or Afghanistan, pro-Americanism is notably lower, leaving more room for

people to update their beliefs. It is in these countries where donor visibility may, on balance, have a

greater effect on improving attitudes among people exposed to the treatment. Existing literature from

these contexts is inconclusive. Andrabi and Das (2010) argue that humanitarian relief helped to improve

attitudes toward foreigners in northern Pakistan, while Zürcher, Koehler, and Böhnke (2010) find null

effects of foreign assistance on attitudes toward foreigners in Afghanistan. Better understanding how

different contexts alter the effects that we have identified here will help inform both our understanding of

attitudinal change and donor policies on public diplomacy.

 

 

 

 

   

 

26  

Tables and Figures  

Figure 1. Screenshots from control and treatment videos

 

 

 

 

 

 

                                                 

 

27  

 

Table 1. Perceived origins of health clinic financing  

Do you know from where the Smiling Sun Clinics get most of their financial assistance?

Don’t Know

United States

Unnamed Foreign Aid

Specific, Non-U.S. Foreign Aid

Govern-ment

Other

Overall Control Group (N=1,073)

0.81 0.11 0.04 0.0009 0.03 0.008

Overall Treatment Group (N=1,163)

0.69 0.24 0.03 0.0009 0.03 0.004

Previous Users Control Group (N=168)

0.65 0.26 0.06 0.01 0.01 0.01

Previous Users Treatment Group (N=198)

0.49 0.40 0.07 0.01 0.04 0.00

Previous Awareness (But Not Use) Control Group (N=332)

0.74 0.16 0.06 0.00 0.03 0.01

Previous Awareness (But Not Use) Treatment Group (N=376)

0.60 0.34 0.03 0.00 0.03 0.003

High Education Control Group (N=251)

0.72 0.21 0.06 0.00 0.02 0.004

High Education Treatment Group (N=263)

0.47 0.44 0.05 0.004 0.04 0.00

Notes: High education is defined as respondents with a completed secondary education or above. p-value for a Pearson χ2 test of independence between the rows and the columns: p < 0.01 for overall, previously aware, and high education; p < 0.05 for previous users.

   

 

28  

Table 2. Responses to foreign policy questions Outcome Control

(Smiling Sun Brand

Only)

Treatment (USAID

Brand and Information)

Difference p-value for H0: No

Difference

U.S. Influence: Negative, Null, or Positive (-1, 0, 1)

0.80 (0.02)

N=1,086

0.84 (0.01)

N=1,177

0.05 (0.02)

0.03 [0.01]

Should Bangladesh trade more with U.S. or else diversify its trading partners? (proportion saying “trade more”)

0.16 (0.01)

N=1,024

0.17 (0.01)

N=1,111

0.01 (0.02)

0.41 [0.40]

Bangladeshi troops serve under the command of military officers from other countries in U.N. PKOs. Which country would you want? (Proportion of respondents with any answer saying “United States”)

0.03 (0.006) N=761

0.02 (0.005) N=845

-0.007 (0.007)

0.42 [0.42]

Notes: Cells present mean responses in each treatment group with standard errors in parentheses. p-values are based on t-tests of H0: No difference in means between the treatment and control groups. p-values in brackets are based on Wilcoxon rank sum tests.

   

 

29  

Table 3. Heterogeneous treatment effects for perceived influence of U.S

U.S.  Has  a  Good  Influence  on  Bangladesh  (-­‐1,  0,  1)  

Previous  Awareness  of  SSC  

No  Previous  Awareness  of  SSC  

Completed  Secondary  or  Above  

Less  Than  Completed  Secondary  

Treatment  (USAID  Brand  and  Information)  

0.87  (0.02)  N=575  

0.82  (0.02)  N=602  

0.88  (0.03)  N=269  

0.83  (0.02)  N=908  

Control  (Smiling  Sun  Brand  Only)   0.84  (0.02)  N=500  

0.75  (0.02)  N=586  

0.86  (0.03)  N=252  

0.77  (0.02)  N=834  

Difference   0.03  (0.03)  

0.06  (0.03)  

0.01  (0.04)  

0.06  (0.02)  

p-­‐value  for  H0:  No  Difference   0.37  [0.26]  

0.04  [0.02]  

0.77  [0.82]  

0.02  [0.02]  

p-­‐value  for  H0:  No  Difference  between  CATEs  

0.37   0.49  

Notes: Outcome variable is coded -1 for respondents who think that the U.S. has a large and negative influence on Bangladesh, 0 for respondents who think that the U.S. does not have an influence on Bangladesh, and 1 for respondents who think that the U.S. has a large and positive influence on Bangladesh. Cells present mean responses in each treatment group with standard errors in parentheses. The p-values are based on t-tests of H0: No difference in means between the treatment and control groups. p-values in brackets are based on Wilcoxon rank sum tests. p-value for difference between conditional average treatment effects (CATEs) is based on a randomization-inference-based test of the kind described in Gerber and Green (2012).

 

30  

Table 4. Responses to commercial questions

Outcome   Control  (Smiling  Sun  Brand  Only)  

Treatment  (USAID  

Brand  and  Information)  

Difference   p-­‐value  for  H0:  No  

Difference  

Trust  in  Brand:  Coca-­‐Cola  (1  –  7)   4.47  (0.06)  

N=1,086  

4.53  (0.05)  

N=1,177  

0.06  (0.08)  

0.45  [0.49]  

Trust  in  Brand:  Apple  (1  –  7)   3.60  (0.07)  

N=1,086  

3.87  (0.06)  

N=1,177  

0.25  (0.09)  

0.01  [0.02]  

Number  of  six  products  (e.g.,  cars,  mobile  phones)  for  which  respondents  thought  U.S.  made  “highest  quality  products”  (0  –  6)  

0.44  (0.03)  N=972  

0.50  (0.03)  

N=1,085  

0.06  (0.04)  

0.14  [0.22]  

Notes: Cells present mean responses in each treatment group with standard errors in parentheses. The p-values are based on t-tests of H0: No difference in means between the treatment and control groups. p-values in brackets are based on Wilcoxon rank sum tests.

                                               

 

31  

Table 5. Responses to domestic government legitimacy questions Outcome   Control  

(Smiling  Sun  Brand  Only)  

Treatment  (USAID  

Brand  and  Information)  

Difference   p-­‐value  for  H0:  No  

Difference  

Level  of  Confidence  in  National  Government  (no,  partial,  or  full,  1  –  3)  

2.36  (0.02)  

N=1,086  

2.38  (0.02)  

N=1,177  

0.01  (0.03)  

0.72  [0.33]  

Level  of  Confidence  in  Local  Government  (no,  partial,  or  full,  1  –  3)  

2.18  (0.02)  

N=1,086  

2.24  (0.02)  

N=1,177  

0.07  (0.03)  

0.02  [0.02]  

Additive  Index  of  Confidence  in  Seven  Government  /  Political  Institutions  (0  –  14)  

8.37  (0.09)  

N=1,086  

8.62  (0.09)  

N=1,177  

0.25  (0.13)  

0.06  [0.07]  

Tax  department  has  the  right  to  make  people  pay  taxes  (strongly  disagree  …  strongly  agree,  1  –  5)  

4.08  (0.03)  

N=1,019  

4.09  (0.03)  

N=1,102  

0.00  (0.04)  

0.91  [0.59]  

Those  who  govern  the  country  are  engaged  in  rampant  corruption  (strongly  disagree  …  strongly  agree,  1  –  5)  

3.61  (0.03)  

N=1,086  

3.50  (0.03)  

N=1,177  

-­‐0.10  (0.05)  

0.03  [0.06]  

Notes: Cells present mean responses in each treatment group with standard errors in parentheses. The p-values are based on t-tests of H0: No difference in means between the treatment and control groups. p-values in brackets are based on Wilcoxon rank sum tests.

   

 

32  

Table 6. Heterogeneous treatment effects for level of confidence in local government

Level  of  Confidence  in  Local  Government  (no,  partial,  or  full,  1  –  3)  

Previous  Use  of  SSC  

No  Previous  Use  of  SSC  

Politically  Active  

Not  Politically  Active  

Completed  Secondary  or  Above  

Less  Than  Completed  Secondary  

Treatment  (USAID  Brand  and  Information)  

2.35  (0.04)  N=201  

2.22  (0.02)  N=976  

2.28  (0.04)  N=192  

2.23  (0.02)  N=985  

2.34  (0.04)  N=269  

2.21  (0.02)  N=908  

Control  (Smiling  Sun  Brand  Only)  

2.09  (0.05)  N=170  

2.19  (0.02)  N=916  

2.06  (0.05)  N=185  

2.20  (0.02)  N=901  

2.18  (0.04)  N=252  

2.18  (0.02)  N=834  

Difference   0.26  (0.06)  

0.03  (0.03)  

0.22  (0.06)  

0.03  (0.03)  

0.16  (0.06)  

0.04  (0.03)  

p-­‐value  for  H0:  No  Difference  

0.01  [0.01]  

0.40  [0.32]  

0.01  [0.01]  

0.26  [0.19]  

0.01  [0.01]  

0.25  [0.20]  

p-­‐value  for  H0:  No  Difference  between  CATEs  

0.01   0.01   0.14  

Notes: Cells present mean responses in each treatment group with standard errors in parentheses. The p-values are based on t-tests of H0: No difference in means between the treatment and control groups. p-values in brackets are based on Wilcoxon rank sum tests. p-value for difference between conditional average treatment effects (CATEs) is based on a randomization-inference-based test of the kind described in Gerber and Green (2012).

 

 

                               

       

 

33  

Appendix

Sampling Strategy

The primary sampling unit (PSU) for the survey was the mouza, an administrative unit roughly equivalent

to a village. We randomly sampled 96 mouza using a probability-proportional-to-size weighting based on

the 2011 population census. The 96 mouza cover 53 out of 64 districts in Bangladesh. For each PSU, 25

households were selected using a random walk where every tenth household was chosen. Within each

household, a Kish grid was used to identify a respondent over the age of 18. Not all sampled units were

eventually surveyed, resulting in a total N of 2,294, rather than the anticipated N of 2,400.

Table A1. Balance Checks Variable Control Group

Mean (SE) Treatment

Group Mean (SE)

Difference (SE) p-value for H0: No Difference

Female (0/1) 0.54 (0.02)

N=1,076

0.51 (0.01)

N=1,167

0.03 (0.02)

0.13

Age 37.6 (0.42)

N=1,086

37.9 (0.41)

N=1,177

-0.29 (0.59)

0.62

Education (7-point scale) 3.82 (0.06)

N=1,086

3.92 (0.06)

N=1,177

-0.10 (0.09)

0.25

Minority Group (0/1) 0.17 (0.01)

N=1,086

0.15 (0.01)

N=1,177

0.02 (0.02)

0.27

Importance of Health Care as a Policy Issue (4-point scale)

3.80 (0.01)

N=1,082

3.81 (0.01)

N=1,174

-0.02 (0.02)

0.30

Previously Used Smiling Sun Clinic (0/1)

0.16 (0.01)

N=1,086

0.17 (0.01)

N=1,177

-0.01 (0.02)

0.37

Previously Aware of Smiling Sun Clinics (0/1)

0.46 (0.02)

N=1,086

0.49 (0.01)

N=1,177

-0.03 (0.02)

0.19

 

34  

Knowledge of U.S. Funding in the Control Group

Among the control group, we run a regression predicting whether or not the respondents correctly

answered the question about the funding of the Smiling Sun Clinics by identifying the United States as the

funder.

Table A2. Control Group Knowledge of U.S. Funding

U.S. Provided Funding for SSCs

(1)

Previous Clinic Users 1.02*** (0.30)

Previously Aware of the Clinic 0.88*** (0.28)

Female -0.53** (0.24)

Age -0.02* (0.01)

Education Level 0.16** (0.07)

Minority Group Status 0.66** (0.30)

Income 0.06 (0.05)

Subjective Quality of Life Assessment

0.42* (0.23)

Access to a Television 0.29 (0.29)

Upazilla Fixed Effects Y N 970 Pseudo-R2 0.15

Notes: Results from a logistic regression. *** - p < 0.01; ** - p < 0.05; * - p < 0.10.

 

35  

Regression-Based Tests

In the paper, we rely on difference-in-means tests for all of our results. Here we look for treatment effects

in regression models that control for socio-demographic characteristics of our respondents. Specifically,

we control for whether or not the respondent is a woman, age, education, whether or not the respondent

belongs to an ethnic minority group, self-reported household income, the respondent’s subjective

assessment of their family’s living conditions, an indicator for whether or not the respondent has access to

a television, and upazilla (subdistrict) fixed effects.

The regression models return coefficients of similar magnitude and levels of statistical significance to the

difference-in-means tests reported in the main text.

 

36  

Outcome Type of Regression

Model

Coefficient on Treatment Indicator

(S.E.)

N

Recognition of U.S. Brand Identify U.S. as Funder of Smiling Sun Clinics (0/1) Logistic 0.97***

(0.13) 2,243

Foreign Policy Outcomes U.S. Influence: Negative, Null, or Positive (-1, 0, 1) Ordered

Logistic 0.33*** (0.13)

2,243

Should Bangladesh trade more with U.S. or else diversify its trading partners? (0/1)

Logistic 0.12 (0.12)

2,117

Bangladeshi troops serve under the command of military officers from other countries in U.N. PKOs. Which country would you want? (For respondents with any answer, whether they said “United States”, 0/1)

Logistic -0.40 (0.35)

1,594

Commercial Outcomes Trust in Brand: Coca-Cola (1-7) Ordered

Logistic -0.004 (0.08)

2,243

Trust in Brand: Apple (1-7) Ordered Logistic

0.15** (0.08)

2,243

Number of Six Products for Which Respondent Thought that U.S. Made “High Quality” Goods (0 -6)

Ordered Logistic

0.15 (0.10)

2,039

Domestic Government Legitimacy Level of Confidence in National Government (no, partial, or full, 1 – 3)

Ordered Logistic

0.08 (0.08)

2,243

Level of Confidence in Local Government (no, partial, or full, 1 – 3)

Ordered Logistic

0.20** (0.08)

2,243

Additive Index of Confidence in Seven Government / Political Institutions (0 – 14)

Ordered Logistic

0.14* (0.07)

2,243

Tax department has the right to make people pay taxes (strongly disagree … strongly agree, 1 – 5)

Ordered Logistic

0.04 (0.09)

2,103

Those who govern the country are engaged in rampant corruption (strongly disagree … strongly agree, 1 – 5)

Ordered Logistic

-0.14* (0.07)

2,243

Table A3. Regression-Based Analyses. Coefficient and standard error from (ordered) logistic regression models including an indicator for female respondents, age, education, an indicator for ethnic minority status, household income, subjective assessment of living conditions, an indicator for access to a television, and upazila (sub-district) fixed effects.

 

37  

Specific Movement in Outcome Variables

Table A4. Cross-Tabs of U.S. Influence Variable and Treatment Control Treatment Large and Negative Influence 0.05 0.04 Small Influence 0.11 0.07 Large and Positive Influence 0.84 0.88 Notes: Proportion of cases in each cell. p < 0.02 in a χ2 test of independence between rows and columns.

Table A5. Cross-Tabs of Local Government Confidence Variable and Treatment

Control Treatment No Confidence 0.14 0.13 Partial Confidence 0.55 0.50 Full Confidence 0.31 0.37 Notes: Proportion of cases in each cell. p < 0.02 in a χ2 test of independence between rows and columns.

 

38  

Estimation of Complier Average Causal Effects

We estimate complier average causal effects (CACEs) based on observed and assumed patterns of non-

compliance. In Table 2 in the paper we provide an estimate of non-compliance in the control group: 11

percent of the respondents in the control group say that the United States funds the SSCs, even though

this group had not been given the information that identified the United States as sponsor of the

intervention. Although our survey instrument included a manipulation check for the identification of the

logo, we did not include the same check for the information treatment and are thus unable to present

exact numbers as to how many respondents in the treatment group were unable to identify the United

States as the SSC sponsor. Therefore, we estimate a series of CACEs assuming different proportions of

non-compliance (e.g., 10%, 15%, 20%, 25%, 30%, and 35%) in the treatment group.

Using the CACE theorem for two-sided non-compliance (see Gerber and Green 2014, p. 183-186), we

estimate the CACE by dividing the ITT by the difference in the proportions of compliers in treatment and

control groups. The CACE provides consistent estimates of the average treatment effect among

compliers.26 In Tables A6 and A7 we show CACEs across six assumed proportions of non-compliers for

our measure that captures positive attitudes toward the United States and our measure of trust in local

government.

Table A6 lists the estimates across assumed levels of non-compliance for respondent positive attitudes

toward the United States. Given the non-compliers in the control group, the size of the CACE increases

slightly as we assume more and more non-compliers in the treatment group.27 Across different

proportions of non-compliance among the treated, the average treatment effect among compliers is

statistically significant. For example, if we assume that 10 percent of the respondents in the treatment

group did not actually receive the treatment, we would expect our information treatment to improve

perceptions of the United States among those who did receive the treatment by 0.06 points (on a three-

point scale). If we assume that 20 percent of respondents did not receive the treatment, then we estimate

the CACE to be a 0.07 point improvement. These estimates suggest that even among those who fully

absorbed the information about U.S. funding, the extent to which we can observe more positive attitudes

about the United States is constrained – a result of the high levels of baseline belief that the United States

has a positive influence on Bangladesh.

                                                                                                                         26 We estimate the CACE under assumptions of monotonicity, non-interference, and excludability. 27 This relationship holds as long as the proportion of treatment compliers is greater than the proportion of non-compliers in the control.    

 

39  

Table A6. CACEs for Perceived Influence of U.S.

Outcome: Perceived Influence of U.S. Assuming the following proportion of non-compliance within the treatment group…

CACE Robust Std. Error

0.10 0.060 0.262 0.15 0.064 0.280 0.20 0.068 0.030 0.25 0.074 0.032 0.30 0.081 0.035 0.35 0.089 0.038

Notes: Outcome variable is coded -1 for respondents who think that the U.S. has a large and negative influence on Bangladesh, 0 for respondents who think that the U.S. does not have an influence on Bangladesh, and 1 for respondents who think that the U.S. has a large and positive influence on Bangladesh. Coefficient and standard error are based on 2SLS estimates described in Gerber and Green (2012).

Table A7 lists the estimates across assumed levels of non-compliance for respondent’s trust in their local

government. Given the non-compliers in the control group, the size of the CACE (again) increases as we

increase the assumed proportion of non-compliers in the treatment group. For example, if we assume that

10 percent of the respondents in the treatment group did not actually receive the treatment, we would

expect our information treatment to increase trust in the local government by 0.08 points. If we assume

that 20 percent of respondents did not receive the treatment, then we estimate the CACE to be a 0.09

point improvement.

Table A7. CACEs for Perceived Trust in Local Government

Outcome: Perceived Trust in Local Gov’t Assuming the following proportion of non-compliance within the treatment group…

CACE Robust Std. Error

0.10 0.082 0.349 0.15 0.088 0.373 0.20 0.095 0.040 0.25 0.102 0.043 0.30 0.111 0.046 0.35 0.121 0.051

Notes: Coefficient and standard error are based on 2SLS estimates described in Gerber and Green (2012).

 

40  

Treatment Effects among Women Given the substantive content of the video, we might expect that women might be more sensitive to the

information about the funding of the Smiling Sun Clinics. The results reveal that, if anything, our treatment

effects are concentrated among our male respondents. Treatment effects among women are often

smaller and less likely to be statistically significant.

Table A8. Replication of Table 1 (Perceived Origins of Health Clinic Financing) for Women Only

Do you know from where the Smiling Sun Clinics get most of their financial assistance?

Don’t Know

United States

Unnamed

Foreign Aid

Specific, Non-U.S.

Foreign Aid

Govern-ment

Other

Overall Control Group (N=578)

0.81 0.11 0.04 0.002 0.03 0.009

Overall Treatment Group (N=587)

0.71 0.21 0.03 0.002 0.04 0.005

Previous Users Control Group (N=93)

0.63 0.29 0.05 0.01 0.01 0.00

Previous Users Treatment Group (N=117)

0.50 0.35 0.09 0.01 0.05 0.00

Previous Awareness (But Not Use) Control Group (N=164)

0.78 0.12 0.05 0.05 0.04 0.01

Previous Awareness (But Not Use) Treatment Group (N=172)

0.67 0.26 0.02 0.02 0.05 0.01

High Education Control Group (N=114)

0.70 0.22 0.04 0.00 0.04 0.00

High Education Treatment Group (N=97)

0.48 0.37 0.07 0.01 0.06 0.00

Notes: High education is defined as respondents with a completed secondary education or above. p-value for a Pearson χ2 test of independence between the rows and the columns: p < 0.01 for overall; p < 0.25 for previous users; p < 0.03 for previously aware; and p < 0.03 for high education.

 

41  

Table A9. Replication of Table 2 (Responses to Foreign Policy Questions) for Women Only

Outcome Control

(Smiling Sun Brand

Only)

Treatment (USAID

Brand and Information

)

Difference p-value for H0: No

Difference

U.S. Influence: Negative, Null, or Positive (-1, 0, 1)

0.79 (0.02) N=583

0.83 (0.02) N=595

0.04 (0.03)

0.14 [0.09]

Should Bangladesh trade more with U.S. or else diversify its trading partners? (proportion saying “trade more”)

0.18 (0.02) N=542

0.18 (0.02) N=547

0.00 (0.02)

0.99 [0.99]

Bangladeshi troops serve under the command of military officers from other countries in U.N. PKOs. Which country would you want? (Proportion of respondents with any answer saying “United States”)

0.03 (0.009) N=359

0.00 (0.00) N=394

-0.03 (0.008)

0.01 [0.01]

Notes: Cells present mean responses in each treatment group with standard errors in parentheses. p-values are based on t-tests of H0: No difference in means between the treatment and control groups. p-values in brackets are based on Wilcoxon rank sum tests.

Table A10. Replication of Table 4 (Responses to Commercial Questions) for Women Only

Outcome Control (Smiling

Sun Brand Only)

Treatment (USAID

Brand and Information

)

Difference p-value for H0: No

Difference

Trust in Brand: Coca-Cola (1 – 7) 4.37 (0.08) N=583

4.49 (0.08) N=595

0.12 (0.11)

0.25 [0.25]

Trust in Brand: Apple (1 – 7) 3.49 (0.09) N=583

3.68 (0.09) N=595

0.19 (0.12)

0.13 [0.16]

Number of six products (e.g., cars, mobile phones) for which respondents thought U.S. made “highest quality products” (0 – 6)

0.43 (0.04) N=501

0.50 (0.03) N=529

0.07 (0.06)

0.27 [0.31]

Notes: Cells present mean responses in each treatment group with standard errors in parentheses. The p-values are based on t-tests of H0: No difference in means between the treatment and control groups. p-values in brackets are based on Wilcoxon rank sum tests.

 

42  

Table A11. Replication of Table 5 (Responses to Domestic Government Legitimacy Questions) for Women Only

Outcome Control (Smiling

Sun Brand Only)

Treatment (USAID

Brand and Information

)

Difference p-value for H0: No

Difference

Level of Confidence in National Government (no, partial, or full, 1 – 3)

2.40 (0.03) N=583

2.39 (0.03) N=595

-0.01 (0.04)

0.81 [0.87]

Level of Confidence in Local Government (no, partial, or full, 1 – 3)

2.19 (0.03) N=583

2.23 (0.03) N=595

0.04 (0.04)

0.31 [0.30]

Additive Index of Confidence in Seven Government / Political Institutions (0 – 14)

8.50 (0.12) N=583

8.64 (0.12) N=595

0.14 (0.17)

0.43 [0.52]

Tax department has the right to make people pay taxes (strongly disagree … strongly agree, 1 – 5)

4.09 (0.04) N=537

4.06 (0.04) N=542

-0.03 (0.05)

0.59 [0.99]

Those who govern the country are engaged in rampant corruption (strongly disagree … strongly agree, 1 – 5)

3.63 (0.04) N=583

3.56 (0.05) N=595

-0.07 (0.06)

0.27 [0.34]

Notes: Cells present mean responses in each treatment group with standard errors in parentheses. The p-values are based on t-tests of H0: No difference in means between the treatment and control groups. p-values in brackets are based on Wilcoxon rank sum tests.

 

43  

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